The present invention relates to using computer vision systems, methods or algorithms to search video image data for objects as a function of pose or motion direction attributes.
Objects and their motion activities as represented in video data may be described through semantic attributes and concepts. Illustrative but not exhaustive examples of object semantics include object color, size, length, width, height, speed, direction of travel, date, time, location of object, as well as measurements from non-visual sensors (for example sound, weight, physical texture, displacement, pressure differentials, radioactive emissions, chemical profile and other data sensors). Accordingly, objects may be defined as representations of one or more of their semantic attribute values, wherein video data may be searched for occurrences of an object, including as distinguished from other objects, by using computer vision applications to search for associated semantic attribute modeling or representations. Examples include face verification and people search applications and tripwire alarm systems, and vehicle tracking and traffic monitoring systems.
However, discernment of objects and their motion patterns from video data by automated video analysis systems and methods may be difficult or unreliable in some environments and applications, for example due to images crowded with multiple objects, fast moving objects, high object occurrence and motion frequencies, image clutter, poor or variable lighting and object resolutions, distracting competing visual information, etc. Object recognition may also be restricted by type, for example models to detect objects of one size may not find objects of other, different sizes.